Opportunity to bridge epidemiology, machine learning, and fieldwork to inform new strategies for controlling schistosomiasis infections in Uganda

**Deadline for Univ. of Oxford funding: January 2020

An estimated 250 million people worldwide have schistosomiasis infections, of which 80% are in sub-Saharan Africa. In 2017, schistosomiasis caused at least 1.43 million disability adjusted life years. Blood flukes (parasitic worms) cause schistosomiasis. Transmission to humans is via contact with freshwater sources that are contaminated through open defecation/urination and harbour competent intermediate snail hosts. Water, sanitation, and hygiene (WASH) interventions are promoted as interventions to reduce pathogen transmission.

Consistently identifying effective WASH interventions that reduce schistosomiasis infection risks is challenging. One reason entails the heterogeneity in human activities that puts individuals at risk of infection. Exposure to parasites varies based on occupation, gender, and household role. And, there are challenges in measuring activities in water. We currently lack the granularity to identify who enters the water at what time for what duration and which activity.

Wearable cameras offer the potential to measure activities from first person perspectives. Previously unrecognized activities that may contribute to parasite exposure could be identified. When layered with infection data, there is the opportunity to identify the composition of activities that most affect the probability of infection and new WASH strategies. This project will undertake the monitoring of activities of a rural village in Uganda, with the aims of capturing image data from individuals aged 1+ years.

Type
PhD position
Institution
University of Oxford
City
Oxford
Country
United Kingdom
Closing date
January 10th, 2020
Posted on
September 18th, 2019 10:32
Last updated
September 18th, 2019 10:32
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